app.py

import gradio as gr
import requests
import os
from fpdf import FPDF
import uuid
import re
import matplotlib.pyplot as plt
import tempfile

# Load your Nebius API key and Folder ID from environment variables
NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY") or "YOUR_NEBIUS_API_KEY"
FOLDER_ID = os.getenv("FOLDER_ID") or "YOUR_FOLDER_ID"

def generate_meal(preferences, ingredients, time):
    prompt = f"""
You're a smart kitchen agent.
Given:
- Dietary preferences: {preferences}
- Ingredients available: {ingredients}
- Time available: {time} minutes
Tasks:
1. Suggest one meal idea
2. Provide step-by-step recipe instructions
3. List missing ingredients (Shopping List)
4. Estimate nutrition (calories, protein, carbs, fat)
Output in this format:
Meal Name: ...
Steps:
1. ...
2. ...
Shopping List:
- ...
Nutrition:
- Calories: ... kcal
- Protein: ... g
- Carbs: ... g
- Fat: ... g
"""
    headers = {
        "Authorization": f"Api-Key {NEBIUS_API_KEY}",
        "Content-Type": "application/json"
    }
    data = {
        "modelUri": f"gpt://{FOLDER_ID}/yandexgpt-lite",
        "completionOptions": {"stream": False, "temperature": 0.7, "maxTokens": 700},
        "messages": [{"role": "user", "text": prompt.strip()}]
    }
    response = requests.post(
        "https://llm.api.cloud.yandex.net/foundationModels/v1/completion",
        headers=headers, json=data
    )
    try:
        text = response.json()["result"]["alternatives"][0]["message"]["text"]
    except Exception as e:
        return f"❌ Error: Could not fetch recipe. {e}"
    return text

def extract_nutrition(recipe_text):
    # More robust pattern allowing newlines and flexible spacing
    pattern = (
        r"Calories:\s*([\d\.]+)\s*kcal.*?"
        r"Protein:\s*([\d\.]+)\s*g.*?"
        r"Carbs:\s*([\d\.]+)\s*g.*?"
        r"Fat:\s*([\d\.]+)\s*g"
    )
    match = re.search(pattern, recipe_text.replace("\n", " "))
    if match:
        calories, protein, carbs, fat = map(float, match.groups())
        return {"Calories": calories, "Protein": protein, "Carbs": carbs, "Fat": fat}
    return None

def plot_nutrition_chart(nutrition):
    fig, ax = plt.subplots()
    nutrients = list(nutrition.keys())
    values = list(nutrition.values())
    colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']
    ax.pie(values, labels=nutrients, autopct='%1.1f%%', startangle=140, colors=colors)
    ax.axis('equal')
    plt.tight_layout()

    temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
    plt.savefig(temp_file.name)
    plt.close(fig)
    return temp_file.name

def handle_generate(preferences, ingredients, time):
    recipe = generate_meal(preferences, ingredients, time)
    nutrition = extract_nutrition(recipe)
    chart_path = None
    if nutrition:
        chart_path = plot_nutrition_chart(nutrition)
    return recipe, chart_path

def save_pdf(recipe_text):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)
    for line in recipe_text.split('\n'):
        pdf.multi_cell(0, 10, line)
    temp_file = tempfile.NamedTemporaryFile(suffix=".pdf", delete=False)
    pdf.output(temp_file.name)
    return temp_file.name

# Optional: placeholder function for speech-to-text (you can replace with real STT)
def dummy_speech_to_text(audio):
    # This just returns a fixed string for demo. Replace with actual STT model call if you want.
    if audio is None:
        return ""
    return "tomato, onion, garlic"

with gr.Blocks(css="""
    body { background-color: #111; color: #eee; font-family: 'Segoe UI', sans-serif; }
    .gradio-container { max-width: 900px; margin: auto; }
    .input-label { font-weight: 600; margin-bottom: 5px; }
    .footer { font-size: 0.8rem; color: #666; padding: 10px; text-align: center; }
    @media (max-width: 600px) {
        .gradio-container { padding: 10px; }
    }
""") as demo:
    gr.Markdown("# 👨‍🍳 AgentChef: AI Recipe Planner & Smart Kitchen Assistant")

    with gr.Row():
        with gr.Column(scale=1, min_width=200):
            preferences = gr.Textbox(label="🥗 Dietary Preferences", placeholder="e.g. vegetarian, keto")
            ingredients = gr.Textbox(label="🧂 Ingredients You Have", placeholder="e.g. rice, tomato, onion", lines=3)
            
            mic = gr.Audio(type="filepath", label="🎤 Speak Ingredients")

            transcribed = gr.Textbox(label="📝 Transcribed Ingredients (from mic)", interactive=False)

            mic.change(dummy_speech_to_text, inputs=mic, outputs=transcribed)
            # Add a button to copy transcribed text to ingredients textbox if needed
            copy_btn = gr.Button("Copy Transcription to Ingredients")
            copy_btn.click(lambda txt: txt, inputs=transcribed, outputs=ingredients)
            
            time = gr.Slider(5, 60, value=20, step=5, label="⏱️ Time Available (minutes)")
            generate_btn = gr.Button("🍽️ Generate Recipe")

        with gr.Column(scale=1, min_width=300):
            recipe_output = gr.Textbox(label="📝 Recipe Output", lines=15, interactive=False)
            nutrition_chart = gr.Image(label="📊 Nutrition Breakdown", interactive=False)
            pdf_btn = gr.Button("📄 Export as PDF")

    generate_btn.click(handle_generate,
                       inputs=[preferences, ingredients, time],
                       outputs=[recipe_output, nutrition_chart])

    pdf_btn.click(save_pdf, inputs=[recipe_output], outputs=gr.File(label="📥 Download Recipe PDF"))

demo.launch()